App-based crowd sourcing of bicycle and pedestrian conflict data : final report.

Details

• Creators:
  Mattingly, Stephen P. ; Casey, Colleen ; Johnson, Taylor
• Corporate Creators:
  University of Texas at Arlington
• Corporate Contributors:
  Western Michigan University. Transportation Research Center for Livable Communities
• Subject/TRT Terms:
  • Data analysis
  • Traffic conflicts
  • Transportation safety
  • Pedestrians
  • Cyclists
  • Mobile applications Conflict analysis
  • Active transportation
  • Crowdsourcing
  • Apps
• Publication/ Report Number:
  TRCLC 15-7
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Western Michigan University. Transportation Research Center for Livable Communities
• Abstract:
  Most agencies and decision-makers rely on crash and crash severity (property damage only, injury or fatality) data ; to assess transportation safety; however, in the context of public health where perceptions of safety may influence ; the willingness to adopt active transportation modes (e.g. bicycling and walking), pedestrian-vehicle and other ; similar conflicts may represent a better performance measure for safety assessment. For transportation safety, a clear ; conflict occurs when two parties’ paths cross and one of the parties must undertake an evasive maneuver (e.g. ; change direction or stop) to avoid a crash. Other less severe conflicts where paths cross but no evasive maneuver ; occurs may also impact public perceptions of safety. Most existing literature on conflicts focuses on vehicle ; conflicts and intersections. While some research has investigated bicycle and pedestrian conflicts, most of this has ; focused on the intersection environment. In this project, we propose field testing a crowd-sourced data app to better ; understand the continuum of conflicts (bicycle/pedestrian, bicycle/vehicle, and pedestrian/vehicle) experienced by ; pedestrians and cyclists; the study also tests the effectiveness of the app and its associated crowd-sourced data ; collection. This study assesses the data quality of the crowd sourced data and compares it to more traditional data ; sources while performing hot spot analysis. If widely adopted, the app will enable communities to create their own ; data collection efforts to identify dangerous sites within their neighborhoods. Agencies will have a valuable data ; source at low-cost to help inform their decision making related to bicycle and pedestrian education, enforcement, ; infrastructure, programs and policies.
  More +
• Format:
  PDF
• Funding:
  TRCLC 14-01
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/32318/dot_32318_DS1.pdf
• File Type:
  [PDF - 7.81 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:71c685c05fcd94a11f70facd76298b860a143dea899b1a06d70ce36c6006873c2c022ae7f88a2d9c87c2ad0947460da6c91b4767336248045bb0d73f6faca71e